Credit Suisse: Making Fat Tails Work for You

Both theory and empirical evidence on the success of certain “modified risk techniques” show that they can do what they are designed to do. They can accommodate fat tail events and diversify a portfolio’s sources of return.

That is the gist of a Credit Suisse white paper prepared by Yogi Thambiah and Nicolo’ Foscari.

The paper is entitled, “New Normal Investing: Is the (Fat) Tail Wagging Your Portfolio?” a title that neatly encapsulates two bits of finance jargon. First, there is the phrase “new normal,” coined by PIMCO in 2009, to express the view that investors and managers shouldn’t wait for any return of the good-old-days of the boom in real estate and its derivatives. They should, rather, accustom themselves to the world that the bust-ups of 2007-08 have created.

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Author Bio:
Christopher Faille is a Jamesian pragmatist. William James has taught him, for example, that "you can say of a line that it runs east, or you can say that it runs west, and the line per se accepts both descriptions without rebelling at the inconsistency."

But consider the recent research published by Artemis Capital Management that calculates the market implied probability of 21% (based on options prices) of a 50% crash in equity prices in one year. This versus their estimate of the historical realized frequency of 2.9%. If anything, fat tails on the downside are dramatically overpriced at this point, and likely not a source of interesting returns. The suggestions in the paper for hedging the downside fat tail are coincident with the pricing observed in the marketplace. It’s a crowded trade.

Why not? It is an extreme event, but why would a single event would ever be considered as defying Gaussian.
I would expect that only a frequency of extreme events would be considered as a deviation from normal distribution.

You raise an important point. Clearly there must be more to a particular “fat tail” strategy than simply the confidence that if one bets on the fatness of tails, all will come out well. It is because so many parties are crowding this trade that alphas are becoming betas, and both are becoming zero. As I recently heard some one say, the search for alpha is like the act of peeling an onion. Every time a given layer is routinized, it becomes beta, and you have to peel deeper.

George,

You, too, raise a good point, and my comment on October 1987 may have seemed facetious. But I was thinking of Mandelbrot’s career, which I referenced in the following paragraph. As Justin Fox has written: back in the mid 1960s, Mandelbrot was part of the “random walk gang,” with Eugene Fama and the rest. He drifted away, though, applying his fractal ideas to other fields, in part because his colleagues in quantitative finance were in his view too enamoured of Gaussian distributions.

He reportedly said of the field around the time the Black-Scholes papers came out, “well, it won’t last. I’ll come back when it’s gone.” The 1987 crash was the precipitating event that caused Mandelbrot again to begin paying attention to finance, and that led some modelers to begin paying him attention in return.

What the CS paper says is basic stuff that should be known by every serious practitioner by now. I simply can’t believe that there is still anyone out there who continues to use mean-variance optimization… And regarding their conclusion, it is so blantantly self-serving that the whole paper can only be aimed to their own unsophisticated client base.